Induction and Ferroelectric Switching of Flux Closure Domains in Strained PbTiO3 with Neural Network Quantum Molecular Dynamics.

We have developed an extension of the Neural Network Quantum Molecular Dynamics (NNQMD) simulation method to incorporate electric-field dynamics based on Born effective charge (BEC), called NNQMD-BEC. We first validate NNQMD-BEC for the switching mechanisms of archetypal ferroelectric PbTiO3 bulk crystal and 180° domain walls (DWs). NNQMD-BEC simulations correctly describe the nucleation-and-growth mechanism during DW switching. In triaxially strained PbTiO3 with strain conditions commonly seen in many superlattice configurations, we find that flux-closure texture can be induced with application of an electric field perpendicular to the original polarization direction. Upon field reversal, the flux-closure texture switches via a pair of transient vortices as the intermediate state, indicating an energy-efficient switching pathway. Our NNQMD-BEC method provides a theoretical guidance to study electro-mechano effects with existing machine learning force fields using a simple BEC extension, which will be relevant for engineering applications such as field-controlled switching in mechanically strained ferroelectric devices.

Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics.

The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids.

Ferromagnetic metal conversion directly from two-dimensional nickel hydroxide.

We have demonstrated a direct metallic conversion from nickel hydroxide nanosheets to nickel metal nanostructures by thermal annealing in vacuum. The metal transition of the single-layer nanosheets deposited on a Si substrate was revealed by x-ray absorption near edge structure (XANES) measurements. The XANES signal significantly changed at annealing temperatures above 250 °C. The metal transition temperature coincides with the reported temperatures at which layered nickel hydroxide nanosheets are converted to nickel oxide nanosheets by calcination in air. Auger measurements confirmed that a dissociation of oxygen from the hydroxide nanosheet induces the metallic conversion. The converted nickel metallic structures exhibit ferromagnetic behavior revealed by x-ray magnetic circular dichroism (XMCD) measurement. Atomic force microscopy measurements indicate that diffusions of nickel atoms on the substrates leads to a structural change from a 2D-like structure to a particle-like structure.

Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides.

We examined the estimation of thermal conductivity through molecular dynamics simulations for a superionic conductor, α-Ag2Se, using the interatomic potential based on an artificial neural network (ANN potential). The training data were created using the existing empirical potential of Ag2Se to help find suitable computational and training requirements for the ANN potential, with the intent to apply them to first-principles calculations. The thermal conductivities calculated using different definitions of heat flux were compared, and the effect of explicit long-range Coulomb interaction on the conductivities was investigated. We clarified that using a rigorous heat flux formula for the ANN potential, even for highly ionic α-Ag2Se, the resulting thermal conductivity was reasonably consistent with the reference value without explicitly considering Coulomb interaction. It was found that ANN training including the virial term played an important role in reducing the dependency of thermal conductivity on the initial values of the weight parameters of the ANN.

Optical Control of Non-Equilibrium Phonon Dynamics.

The light-induced selective population of short-lived far-from-equilibrium vibration modes is a promising approach for controlling ultrafast and irreversible structural changes in functional nanomaterials. However, this requires a detailed understanding of the dynamics and evolution of these phonon modes and their coupling to the excited-state electronic structure. Here, we combine femtosecond mega-electronvolt electron diffraction experiments on a prototypical layered material, MoTe2, with non-adiabatic quantum molecular dynamics simulations and ab initio electronic structure calculations to show how non-radiative energy relaxation pathways for excited electrons can be tuned by controlling the optical excitation energy. We show how the dominant intravalley and intervalley scattering mechanisms for hot and band-edge electrons leads to markedly different transient phonon populations evident in electron diffraction patterns. This understanding of how tuning optical excitations affect phonon populations and atomic motion is critical for efficiently controlling light-induced structural transitions of optoelectronic devices.

Ab initio molecular dynamics study of prebiotic production processes of organic compounds at meteorite impacts on ocean.

Recent experiments concerning prebiotic materials syntheses suggest that the iron-bearing meteorite impacts on ocean during Late Heavy Bombardment provided abundant organic compounds associated with biomolecules such as amino acids and nucleobases. However, the molecular mechanism of a series of chemical reactions to produce such compounds is not well understood. In this study, we simulate the shock compression state of a meteorite impact for a model system composed of CO2 , H2 O, and metallic iron slab by ab initio molecular dynamics combined with multiscale shock technique, and clarify possible elementary reaction processes up to production of organic compounds. The reactions included not only pathways similar to the Fischer-Tropsch process known as an important hydrocarbon synthesis in many planetary processes but also those resulting in production of a carboxylic acid. It is also found that bicarbonate ions formed from CO2 and H2 O participated in some forms in most of these observed elementary reaction processes. These findings would deepen the understanding of the full range of chemical reactions that could occur in the meteorite impact events. © 2018 Wiley Periodicals, Inc.

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag2Se.

First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. We present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [g(r)]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for α-Ag2Se were created as an application example, and it has been confirmed that not only g(r) and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 104 acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again.

Electronic Origin of Optically-Induced Sub-Picosecond Lattice Dynamics in MoSe2 Monolayer.

Atomically thin layers of transition metal dichalcogenide (TMDC) semiconductors exhibit outstanding electronic and optical properties, with numerous applications such as valleytronics. While unusually rapid and efficient transfer of photoexcitation energy to atomic vibrations was found in recent experiments, its electronic origin remains unknown. Here, we study the lattice dynamics induced by electronic excitation in a model TMDC monolayer, MoSe2, using nonadiabatic quantum molecular dynamics simulations. Simulation results show sub-picosecond disordering of the lattice upon photoexcitation, as measured by the Debye-Waller factor, as well as increasing disorder for higher densities of photogenerated electron-hole pairs. Detailed analysis shows that the rapid, photoinduced lattice dynamics are due to phonon-mode softening, which in turn arises from electronic Fermi surface nesting. Such mechanistic understanding can help guide optical control of material properties for functionalizing TMDC layers, enabling emerging applications such as phase change memories and neuromorphic computing.

Computational Synthesis of MoS2 Layers by Reactive Molecular Dynamics Simulations: Initial Sulfidation of MoO3 Surfaces.

Transition metal dichalcogenides (TMDC) like MoS2 are promising candidates for next-generation electric and optoelectronic devices. These TMDC monolayers are typically synthesized by chemical vapor deposition (CVD). However, despite significant amount of empirical work on this CVD growth of monolayered crystals, neither experiment nor theory has been able to decipher mechanisms of selection rules for different growth scenarios, or make predictions of optimized environmental parameters and growth factors. Here, we present an atomic-scale mechanistic analysis of the initial sulfidation process on MoO3 surfaces using first-principles-informed ReaxFF reactive molecular dynamics (RMD) simulations. We identify a three-step reaction process associated with synthesis of the MoS2 samples from MoO3 and S2 precursors: O2 evolution and self-reduction of the MoO3 surface; SO/SO2 formation and S2-assisted reduction; and sulfidation of the reduced surface and Mo-S bond formation. These atomic processes occurring during early stage MoS2 synthesis, which are consistent with experimental observations and existing theoretical literature, provide valuable input for guided rational synthesis of MoS2 and other TMDC crystals by the CVD process.

Meteorite impacts on ancient oceans opened up multiple NH3 production pathways.

A recent series of shock experiments by Nakazawa et al. starting in 2005 (e.g. [Nakazawa et al., Earth Planet. Sci. Lett., 2005, 235, 356]) suggested that meteorite impacts on ancient oceans would have yielded a considerable amount of NH3 to the early Earth from atmospheric N2 and oceanic H2O through reduction by meteoritic iron. To clarify the mechanisms, we imitated the impact events by performing multi-scale shock technique-based ab initio molecular dynamics in the framework of density functional theory in combination with multi-scale shock technique (MSST) simulations. Our previous simulations with impact energies close to that of the experiments revealed picosecond-order rapid NH3 production during shock compression [Shimamura et al., Sci. Rep., 2016, 6, 38952]. It was also shown that the reduction of N2 took place with an associative mechanism as seen in the catalysis of nitrogenase enzymes. In this study, we performed an MSST-AIMD simulation to investigate the production by meteorite impacts with higher energies, which are closer to the expected values on the early Earth. It was found that the amount of NH3 produced further increased. We also found that the increased NH3 production is due to the emergence of multiple reaction mechanisms at increased impact energies. We elucidated that the reduction of N2 was not only attributed to the associative mechanism but also to a dissociative mechanism as seen in the Haber-Bosch process and to a mechanism through a hydrazinium ion. The emergence of these multiple production mechanisms capable of providing a large amount of NH3 would support the suggestions from recent experiments much more strongly than was previously believed, i.e., shock-induced NH3 production played a key role in the origin of life on Earth.

Effects of CO2 adsorption on proton migration on a hydrated ZrO2 surface: an ab initio molecular dynamics study.

Hydration reactions on a carbonate-terminated cubic ZrO2(110) surface were analyzed using ab initio molecular dynamics (AIMD) simulations. After hydration reactions, carbonates were still present on the surface at 500 K. However, these carbonates are very weak conjugate bases and only act as steric hindrance in proton hopping processes between acidic chemisorbed H2O molecules (Zr-OH2) and monodentate hydroxyl groups (Zr-OH-). Similar to a carbonate-free hydrated surface, Zr-OH2, Zr-OH-, and polydentate hydroxyl groups ([double bond splayed left]OH+) were observed, while the ratio of acidic Zr-OH2 was significantly larger than that on the carbonate-free hydrated surface. A thermodynamic discussion and bond property analysis reveal that CO2 adsorption significantly decreases the basicity of surface oxide ions ([double bond splayed left]O), whereas the acidity of Zr-OH2 is not affected. As a result, protons released from [double bond splayed left]OH+ react with Zr-OH- to form Zr-OH2, leading to a deficiency of proton acceptor sites, which decreases the proton conductivity by the hopping mechanism.

Rotation mechanism of methylammonium molecules in organometal halide perovskite in cubic phase: An ab initio molecular dynamics study.

Rotation of methylammonium (CH3NH3 or MA) molecules is believed to govern the excellent transport properties of photocarriers in the MA lead iodide (MAPbI3) perovskite. Of particular interest is its cubic phase, which exists in industrially important films at room temperature. In order to investigate the rotational behaviors of the MA molecules, we have performed ab initio molecular dynamics simulations of cubic-MAPbI3 at room temperature. There are two types of rotational motions of MA molecules in a crystalline PbI3 cage: reorientation of a whole molecule and intramolecular rotation around the C-N bond within MA molecules. Using a cubic symmetry-assisted analysis (CSAA), we found that the prominent orientation of the C-N bond is the crystalline ⟨110⟩ directions, rather than the ⟨100⟩ and ⟨111⟩ directions. Rapid rotation around the C-N bond is also observed, which easily occurs when the rotational axis is parallel to the ⟨110⟩ directions according to the CSAA. To explain the atomistic mechanisms underlying these CSAA results, we have focused on the relation between H-I hydrogen bonds and the orientation of an MA molecule. Here, the hydrogen bonds were defined by population analysis, and it has been found that, while H atoms in the CH3 group (HC) hardly interacts with I atoms, those in the NH3 group (HN) form at least one hydrogen bond with I atoms and their interatomic distances are in a wide range, 2.2-3.7 Å. Based on these findings, we have given a possible explanation to why the ⟨110⟩ directions are preferred. Namely, the atomic arrangement and interatomic distance between MA and surrounding I atoms are most suitable for the formation of hydrogen bonds. In addition to films, these results are potentially applicable to the rotational behaviors in bulk MAPbI3 as well, considering that the atomistic structure and time constants regarding the rotation of MA molecules statistically agree with bulk experiments.

Hydrogen-on-demand using metallic alloy nanoparticles in water.

Hydrogen production from water using Al particles could provide a renewable energy cycle. However, its practical application is hampered by the low reaction rate and poor yield. Here, large quantum molecular dynamics simulations involving up to 16,611 atoms show that orders-of-magnitude faster reactions with higher yields can be achieved by alloying Al particles with Li. A key nanostructural design is identified as the abundance of neighboring Lewis acid-base pairs, where water-dissociation and hydrogen-production require very small activation energies. These reactions are facilitated by charge pathways across Al atoms that collectively act as a "superanion" and a surprising autocatalytic behavior of bridging Li-O-Al products. Furthermore, dissolution of Li atoms into water produces a corrosive basic solution that inhibits the formation of a reaction-stopping oxide layer on the particle surface, thereby increasing the yield. These atomistic mechanisms not only explain recent experimental findings but also predict the scalability of this hydrogen-on-demand technology at industrial scales.

A divide-conquer-recombine algorithmic paradigm for large spatiotemporal quantum molecular dynamics simulations.

We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 10(6)-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of techniques are employed for efficiently calculating the long-range exact exchange correction and excited-state forces. The NAQMD trajectories are analyzed to extract the rates of various excitonic processes, which are then used in KMC simulation to study the dynamics of the global exciton flow network. This has allowed the study of large-scale photoexcitation dynamics in 6400-atom amorphous molecular solid, reaching the experimental time scales.

Bonding and structure of ceramic-ceramic interfaces.

Quantum molecular dynamics simulations of α-Al2O3(0001)/3C-SiC(111) interfaces reveal profound effects of thermal annealing for producing strong interfaces consisting solely of cation-anion bonds and their consequence on interfacial structures. A Si-terminated SiC surface and Al2O3 form a stronger interface (Si-interface) with a Si-O bond density of 12.2 nm(-2), whereas the C interface has an Al-C bond density of 9.46 nm(-2). The interfacial bond strengthening is accompanied by the formation of an Al2O3 interphase with a thickness of 2-8 Å. Such atomistic understanding may help rational interfacial design of high-temperature ceramic composites for broad applications such as power generation systems.

Dynamic asymmetry of self-diffusion in liquid ZnCl2 under pressure: an ab initio molecular-dynamics study.

The static and dynamic properties of liquid ZnCl2 under pressure are investigated by ab initio molecular-dynamics simulations. The pressure range covers ambient to approximately 80 GPa. The ZnCl4 tetrahedra, which are rather stable at ambient pressure, are shown to deform and collapse with increasing pressure while maintaining an almost constant nearest-neighbor distance between Zn and Cl atoms. The average coordination number of Cl atoms around Zn atoms increases monotonically with pressure, from four at ambient pressure to seven at approximately 80 GPa. Although the self-diffusion coefficients of Zn and Cl atoms, d(Zn) and d(Cl), are almost the same at ambient pressure, the difference between them increases with pressure. At around 10 GPa, d(Zn) is about two times larger than d(Cl). Under further compression, this dynamic asymmetry becomes smaller. The microscopic mechanism of the appearance of the dynamic asymmetry is discussed in relation to the pressure dependence of the local structure.

Enhanced charge transfer by phenyl groups at a rubrene/C60 interface.

Exciton dynamics at an interface between an electron donor, rubrene, and a C(60) acceptor is studied by nonadiabatic quantum molecular dynamics simulation. Simulation results reveal an essential role of the phenyl groups in rubrene in increasing the charge-transfer rate by an order-of-magnitude. The atomistic mechanism of the enhanced charge transfer is found to be the amplification of aromatic breathing modes by the phenyl groups, which causes large fluctuations of electronic excitation energies. These findings provide insight into molecular structure design for efficient solar cells, while explaining recent experimental observations.

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag2S.

Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (Ag2S), which is defect-free, omni-directional, and preserving perfect crystallinity. Our first-principles molecular dynamics simulations elucidate the ductile deformation mechanism in monoclinic-Ag2S under six types of shear systems. Planer mass movement of sulfur atoms plays an important role for the remarkable structural recovery of sulfur-sublattice. This in turn arises from a distinctively high symmetry of the anion-sublattice in Ag2S, which is not seen in other brittle silver chalcogenides. Such mechanistic and lattice-symmetric understanding provides a guideline for designing even higher-performance ductile inorganic semiconductors.

Squishing Skyrmions: Symmetry-Guided Dynamic Transformation of Polar Topologies Under Compression.

Mechanical controllability of recently discovered topological defects (e.g., skyrmions) in ferroelectric materials is of interest for the development of ultralow-power mechano-electronics that are protected against thermal noise. However, fundamental understanding is hindered by the "multiscale quantum challenge" to describe topological switching encompassing large spatiotemporal scales with quantum mechanical accuracy. Here, we overcome this challenge by developing a machine-learning-based multiscale simulation framework─a hybrid neural network quantum molecular dynamics (NNQMD) and molecular mechanics (MM) method. For nanostructures composed of SrTiO3 and PbTiO3, we find how the symmetry of mechanical loading essentially controls polar topological switching. We find under symmetry-breaking uniaxial compression a squishing-to-annihilation pathway versus formation of a topological composite named skyrmionium under symmetry-preserving isotropic compression. The distinct pathways are explained in terms of the underlying materials' elasticity and symmetry, as well as the Landau-Lifshitz-Kittel scaling law. Such rational control of ferroelectric topologies will likely facilitate exploration of the rich ferroelectric "topotronics" design space.

Hydrogen Bonding in Liquid Ammonia.

The nature of hydrogen bonding in condensed ammonia phases, liquid and crystalline ammonia has been a topic of much investigation. Here, we use quantum molecular dynamics simulations to investigate hydrogen bond structure and lifetimes in two ammonia phases: liquid ammonia and crystalline ammonia-I. Unlike liquid water, which has two covalently bonded hydrogen and two hydrogen bonds per oxygen atom, each nitrogen atom in liquid ammonia is found to have only one hydrogen bond at 2.24 Å. The computed lifetime of the hydrogen bond is t ≅ 0.1 ps. In contrast to crystalline water-ice, we find that hydrogen bonding is practically nonexistent in crystalline ammonia-I.